A Survey of Methods for Low-Power Deep Learning and Computer Vision
Document Type
Conference Proceeding
Publication Date
2020
Publication Title
IEEE 6th World Forum on Internet of Things (WF-IoT)
Abstract
Deep neural networks (DNNs) are successful in many computer vision tasks. However, the most accurate DNNs require millions of parameters and operations, making them energy, computation and memory intensive. This impedes the deployment of large DNNs in low-power devices with limited compute resources. Recent research improves DNN models by reducing the memory requirement, energy consumption, and number of operations without significantly decreasing the accuracy. This paper surveys the progress of low-power deep learning and computer vision, specifically in regards to inference, and discusses the methods for compacting and accelerating DNN models. The techniques can be divided into four major categories: (1) parameter quantization and pruning, (2) compressed convolutional filters and matrix factorization, (3) network architecture search, and (4) knowledge distillation. We analyze the accuracy, advantages, disadvantages, and potential solutions to the problems with the techniques in each category. We also discuss new evaluation metrics as a guideline for future research.
Recommended Citation
Abhinav Goel, Caleb Tung, Yung-Hsiang Lu, and George K. Thiruvathukal "A Survey of Methods for Low-Power Deep Learning and Computer Vision" 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020, https://doi.org/10.6084/m9.figshare.12021300.v1
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright Statement
Copyright 2019, Abhinav Goel, Caleb Tung, Yung-Hsiang Lu, and George K. Thiruvathukal
Comments
Accepted for publication at IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA 2020. Conference postponed due to COVID-19 but paper is formally accepted.